# US 2019/0170783 A1 · Fall detection system using a combination of accelerometer, audio input and magnetometer > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0170783 A1 - Document ID: US20190170783A1 - Status: Published application - Kind: A1 - Application number: 16/265,779 - Filed: 2019-02-01 - Published: 2019-06-06 - Earliest claimed: 2010-10-04 - Assignees: Nortek Security & Control LLC - Applicants: Nortek Security & Control LLC - Inventors: Ram David Adva Fish; Henry Messenger; Leonid Baryudin; Soroush Salehian Dardashti; Evgenia Goldshtein - Web page: https://patentgrep.com/patent/US20190170783A1 ## CPC - G01P 1/07: Indicating devices, e.g. for remote indication (indicating working conditions of vehicles G07C5/00) - H04M 1/72421: with automatic activation of emergency service functions, e.g. upon sensing an alarm - H04W 4/90: Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS] - H04M 2201/40: using speech recognition - H04M 2250/02: including a Bluetooth® interface - H04M 2250/12: including a sensor for measuring a physical value, e.g. temperature or motion - G01P: MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT (measuring or recording blood flow A61B5/02, A61B8/06; monitoring speed or deceleration of electrically-propelled vehicles B60L3/00; vehicle lighting systems adapted to indicate speed B60Q1/54; determining position or course in navigation, measuring ground distance in geodesy or surveying G01C; combined measuring devices for measuring two or more variables of movement G01C23/00; measuring velocity of sound G01H; measuring velocity of light G01J7/00; measuring direction or velocity of solid objects by reception or emission of radiowaves or other waves and based on propagation effects, e.g. Doppler effect, propagation time, direction of propagation, G01S; measuring speed of nuclear radiation G01T; measuring acceleration of gravity G01V; {measuring or recording the speed of trains B61L23/00; speed indicators incorporated in motor vehicles B60K35/00; measuring frequency or phase G01R; traffic control G08G}) - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A wearable device for detecting a user state is disclosed. The wearable device includes an accelerometer for measuring an acceleration of a user, a magnetometer for measuring a magnetic field associated with the user's change of orientation, a microphone for receiving audio, a memory for storing the audio, and at least one processor communicatively connected to the accelerometer, the magnetometer, the microphone, and the memory. The processor is identified to declare a measured acceleration as a suspected user state, and to categorize the suspected user state based on the stored audio as one of an activity of daily life (ADL), a confirmed user state, or an inconclusive event. ## Claims ### Claim 1 (canceled) ### Claim 2 A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations to: receive a set of measurement data including accelerometer data, magnetometer data, or audio data; receive tags for the set of measurement data, the tags identifying a first set of measurement data tagged as indicative of a fall and a second set of measurement data tagged as indicative of an activity of daily life (ADL); train a fall model and an ADL model using the set of measurement data and the tags; and wherein the fall model and the ADL model are used to classify unclassified measurement data as indicative of a fall or an ADL. ### Claim 3 (depends on 2) The computer readable storage medium of claim 2, wherein the fall model and the ADL model are Gaussian mixture model (GMM) models. ### Claim 4 (depends on 3) The computer readable storage medium of claim 3, wherein the GMM models are trained using an expectation maximization algorithm. ### Claim 5 (depends on 2) The computer readable storage medium of claim 2, wherein the set of measurement data includes prior events classified as fall and ADL events. ### Claim 6 (depends on 2) The computer readable storage medium of claim 2, wherein the instructions further cause the processor to receive additional measurement data including in-use data and update the fall model and the ADL model using the additional measurement data. ### Claim 7 (depends on 2) The computer readable storage medium of claim 2, wherein the instructions further cause the processor to implement Gaussian mixture model (GMM) classifiers on a subset of a selected feature space of events from the set of measurement data. ### Claim 8 (depends on 7) The computer readable storage medium of claim 7, wherein the selected feature space is selected via a sequential forward floating selection (SFFS) algorithm to maximize classifier performance. ### Claim 9 (depends on 2) The computer readable storage medium of claim 2, wherein the instructions further cause the processor to determine a threshold for classifying an event as a fall, and wherein the unknown measurement is classified based on a score for the unknown measurement and the threshold. ### Claim 10 (depends on 2) The computer readable storage medium of claim 2, wherein the set of measurement data includes accelerometer data, magnetometer data, and audio data. ### Claim 11 (depends on 2) The computer readable storage medium of claim 2, wherein to classify the unknown measurement data as indicative of a fall or an ADL, the fall model and the ADL model are to determine a re-confirmation or change of classification from an initial classification received from a wearable device that generated the unknown measurement. ### Claim 12 A method comprising: receiving, at a processor, a set of measurement data including accelerometer data, magnetometer data, or audio data; receiving tags for the set of measurement data, the tags identifying a first set of measurement data tagged as indicative of a fall and a second set of measurement data tagged as indicative of an activity of daily life (ADL); training, using the processor, a fall model and an ADL model using the set of measurement data and the tags; and wherein the fall model and the ADL model are used to classify unclassified measurement data as indicative of a fall or an ADL. ### Claim 13 (depends on 12) The method of claim 12, wherein the fall model and the ADL model are Gaussian mixture model (GMM) models. ### Claim 14 (depends on 13) The method of claim 13, wherein the GMM models are trained using an expectation maximization algorithm. ### Claim 15 (depends on 12) The method of claim 12, wherein the set of measurement data includes prior events classified as fall and ADL events. ### Claim 16 (depends on 12) The method of claim 12, further comprising receiving additional measurement data including in-use data and updating the fall model and the ADL model using the additional measurement data. ### Claim 17 (depends on 12) The method of claim 12, further comprising implementing Gaussian mixture model (GMM) classifiers on a subset of a selected feature space of events from the set of measurement data. ### Claim 18 (depends on 17) The method of claim 17, wherein the selected feature space is selected via a sequential forward floating selection (SFFS) algorithm to maximize classifier performance. ### Claim 19 (depends on 12) The method of claim 12, further comprising determining a threshold for classifying an event as a fall, and wherein the unknown measurement is classified based on a score for the unknown measurement and the threshold. ### Claim 20 (depends on 12) The method of claim 12, wherein the set of measurement data includes accelerometer data, magnetometer data, and audio data. ### Claim 21 (depends on 12) The method of claim 12, wherein to classify the unknown measurement data as indicative of a fall or an ADL, the fall model and the ADL model are to determine a re-confirmation or change of classification from an initial classification received from a wearable device that generated the unknown measurement.